Prediction of Zonisamide Concentration in Pediatric Patients With Epilepsy: A Machine Learning Approach.

objectiveTo construct and validate prediction models for zonisamide (ZNS) concentration in pediatric patients with epilepsy based on 12 machine learning algorithms, to screen for the optimal algorithm, and to provide a scientific basis for the formulation of individualized ZNS dosing regimens in children.

methodsClinical data of patients who underwent ZNS therapeutic drug monitoring at Kunming Children's Hospital from May 2022 to January 2026 were retrospectively collected and randomly divided into a training set and a test set at a ratio of 7:3. Key predictive variables were determined through a multi-stage feature screening strategy (covering correlation analysis, collinearity diagnosis, univariate analysis, Lasso regression, and random forest algorithm). Based on the selected variables, 12 machine learning regression models were constructed to predict ZNS concentration, and grid search combined with 5-fold cross-validation was employed for parameter optimization and performance evaluation. The coefficient of determination (R

resultsThe modeling cohort of this study enrolled 532 pediatric patients who received zonisamide treatment at the Department of Neurology, Kunming Children's Hospital from May 2022 to May 2024. These patients were randomly divided into a training set (375 cases) and an internal validation set (157 cases) at a ratio of 7:3. Additionally, 436 pediatric patients from the same department of the same hospital from June 2024 to January 2026 were included as an external validation cohort. Comparisons of general clinical data, laboratory indicators, and medication-related data among the groups showed no statistically significant differences (all p > 0.05), indicating that the baseline data were balanced and comparable. The median (interquartile range) ZNS concentrations in the training set, internal validation set, and external validation cohort were 9.65 (8.11, 11.98) μg/mL, 9.93 (8.23, 12.06) μg/mL, and 10.77 (7.67, 13.69) μg/mL, respectively. Through multi-stage feature screening, gender, dosage, age, red blood cell count, concomitant medication status, total protein, uric acid, and platelets were identified as key factors influencing ZNS concentration. Among the 12 constructed machine learning models, the Random Forest (RF) algorithm demonstrated the optimal performance: in the training set, R

conclusionThis study successfully constructed and validated a ZNS concentration prediction model for pediatric patients with epilepsy based on the random forest algorithm. The model demonstrated high precision, strong stability, and good generalization ability. Gender, dose, uric acid, and total protein are core variables influencing ZNS concentration. The findings can provide a reference for the formulation of individualized ZNS dosing regimens in children.